{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2333d7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import  MinMaxScaler, LabelEncoder,OneHotEncoder\n",
    "\n",
    "import itertools\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from collections import namedtuple\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "029ea874",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.read_csv(r\"D:\\Network data\\rs_data\\推荐系统\\ml-1m\\ratings.dat\",sep='::',header=None,names=['user_id','movie_id','rating','time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b463073",
   "metadata": {},
   "outputs": [],
   "source": [
    "rnames = ['movie_id','user_id','rating','release_year','user_avg_rating','user_rating_stddev','user_genre1']\n",
    "data = pd.read_csv(r\"data/trainingSamples.csv\",usecols=['userId','movieId','rating','releaseYear','userGenre1','userAvgRating','userRatingStddev'])\n",
    "data.columns=rnames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fe660f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.fillna({'user_genre1': 'Action'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8b7dc77",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(r\"data/testSamples.csv\",usecols=['userId','movieId','rating','releaseYear','userGenre1','userAvgRating','userRatingStddev'])\n",
    "test_data.columns=rnames\n",
    "test_data.fillna({'user_genre1': 'Action'},inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79a4ebf",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e9bf6b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_vocab = ['Film-Noir', 'Action', 'Adventure', 'Horror', 'Romance', 'War', 'Comedy', 'Western', 'Documentary',\n",
    "               'Sci-Fi', 'Drama', 'Thriller',\n",
    "               'Crime', 'Fantasy', 'Animation', 'IMAX', 'Mystery', 'Children', 'Musical']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d8af94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bcf9edf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneHotEncoder(categories='auto', drop=None, dtype=<class 'numpy.float64'>,\n",
       "              handle_unknown='error', sparse=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.fit(np.array(genre_vocab).reshape(-1,1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4733c510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0.])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc.transform(np.array(['Children','Crime']).reshape(-1,1)).toarray()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d1ffb20b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1926,\n",
       " 1932,\n",
       " 1933,\n",
       " 1934,\n",
       " 1935,\n",
       " 1936,\n",
       " 1937,\n",
       " 1938,\n",
       " 1939,\n",
       " 1940,\n",
       " 1941,\n",
       " 1942,\n",
       " 1943,\n",
       " 1944,\n",
       " 1945,\n",
       " 1946,\n",
       " 1947,\n",
       " 1948,\n",
       " 1949,\n",
       " 1950,\n",
       " 1951,\n",
       " 1952,\n",
       " 1953,\n",
       " 1954,\n",
       " 1955,\n",
       " 1956,\n",
       " 1957,\n",
       " 1958,\n",
       " 1959,\n",
       " 1960,\n",
       " 1961,\n",
       " 1963,\n",
       " 1964,\n",
       " 1965,\n",
       " 1967,\n",
       " 1968,\n",
       " 1969,\n",
       " 1970,\n",
       " 1972,\n",
       " 1973,\n",
       " 1974,\n",
       " 1975,\n",
       " 1976,\n",
       " 1977,\n",
       " 1980,\n",
       " 1981,\n",
       " 1982,\n",
       " 1985,\n",
       " 1986,\n",
       " 1987,\n",
       " 1988,\n",
       " 1989,\n",
       " 1990,\n",
       " 1991,\n",
       " 1992,\n",
       " 1993,\n",
       " 1994,\n",
       " 1995,\n",
       " 1996,\n",
       " 1997,\n",
       " 1998]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(list(data['release_year'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "499035e7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26ee52442c8>]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SsHTpUgQHB2PmzJny2FmzZmHhwoXo3r07IiMjsWjRIgwZMgTjxo0DAAwcOBATJkxARkYGVq5cCQB47LHHMGnSJPTv3x8AkJaWhkGDBiE9PR0vv/wyLl++jEWLFiEjI4MZHiIioha0pls00PgUebvbr6mrtCkIevPNNwEAY8eOdbr//fffx6OPPgoAePrpp1FdXY3Zs2fDYDAgNTUVmzdvRlhYmDx++fLlUCqVmDFjBqqrq3HXXXfhgw8+QEBAgDxm1apVmDdvnryLbMqUKVixYoX8eEBAADZu3IjZs2dj1KhRCAoKwsyZM/HKK6+06QtARETkTxzLYeHXyATJHaN9OBMkCeFomeR/TCYTtFotjEYjs0dEROQXnv+iAP/KPYM5d/TFovH9Wxz33ZES/Pr9PUjuGY6v5t7WiVd4ba76/ObZYURERH6k3NzK3WE8RZ6IiIh8SWsOTwUaCqNrGAQRERGRL3AURl+7Y3R9Johb5ImIiMgXVLRyOSyQp8gTERGRL2nYHda6s8PMtXbY7L65h4pBEBERkR9p6Bh9jZogVUPbGl/tGs0giIiIyI9UtPLssEBVQ4jgqzvEGAQRERH5iRqrDRZbXQfoawVBkiT5/PlhDIKIiIj8hGMpTJKAEPW1D43w9ZPkGQQRERH5CXl7vFoJhUK65nh5hxgzQUREROTNWrs93iHYx0+SZxBERETkJ1rbLdpB7hrNTBARERF5s9Z2i3YI8vHzwxgEERER+YnyVm6Pd3Bkgny1azSDICIiIj/R1uUwR00QC6OJiIjIqzV0i25dJsjXzw9jEEREROQnKsx1NUHXOjfMgX2CiIiIyCe0uSaIHaOJiIjIF7R9i3xdsMRMEBEREXm1cnPbaoKC2DGaiIiIfIGjT1BbO0azMJqIiIi8WpuXw5gJIiIiIl/Q1kxQEM8OIyIiIl9Q0c7dYdVWu9uuqSsxCCIiIvIDNrtAZX1tT5s7RjMTRERERN7KkQUC2tAxmsdmEBERkbcrr+8WrVEqoFa27uOfu8OIiIjI67V1ZxjQqCaIQRARERF5K0cQ1Npzw4BGu8OsNggh3HJdXYlBEBERkR9wHJ4a2pYgqD4TJARgrvW9HWIMgoiIiPxAWw9PBRqCIMA3l8QYBBEREfkBkyMI0rS+JkgZoIA6oC5U8MUdYgyCiIiI/ICjW3RblsOAxl2jGQQRERGRF2prt2gHx5JYDTNBRERE5I3as0UeaOgVxEwQEREReSX58NRWdot2CPThk+QZBBEREfmBCnP7lsN8+fwwBkFERER+wNTO5bAgHz4/jEEQERGRH2hPnyCgoTCaNUFERETkldrTMRpolAliEERERETeqD1nhwG+fZI8gyAiIiIfJ4Ro9xZ57g4jIiIir1VttcFmrzsFPrSNW+TZJ4iIiIi8lqNbdIBCkoOa1mLHaCIiIvJaju3xoRolJElq03OD1HWZI2aCiIiIyOvIh6e2cSkMaMgEsSaIiIiIvE57u0UD3B1GREREXqxhe3zbdoYB3B1GREREXkw+PLUDmSDWBBEREZHXcWSC2totGmjoGM3dYUREROR12ntuGND47DCeIk9ERERepr3dogGeHUZERERerCNb5OXdYVwOIyIiIm/j2CLf1sNTgYblMKtNwGqzu/S6uhqDICIiIh/niuUwwPeyQQyCiIiIfFxHlsPUAQoo6k/aqPGxuqA2B0Hff/89Jk+ejLi4OEiShM8//9zp8UcffRSSJDndhg8f7jTGbDZj7ty5iIqKQkhICKZMmYLz5887jTEYDEhPT4dWq4VWq0V6ejrKysqcxpw9exaTJ09GSEgIoqKiMG/ePFgslrZOiYiIyKeVd6BjtCRJCPbR88PaHARVVlZi6NChWLFiRYtjJkyYgKKiIvm2adMmp8fnz5+P9evXIzMzEzk5OaioqMCkSZNgszV8cWfOnIn8/HxkZWUhKysL+fn5SE9Plx+32Wy49957UVlZiZycHGRmZmLt2rVYuHBhW6dERETk0zqyHAY0dI32tSCozSHhxIkTMXHixKuO0Wg00Ol0zT5mNBrx7rvv4qOPPsK4ceMAAB9//DESEhLw7bffYvz48Th06BCysrKwc+dOpKamAgDefvttjBgxAkeOHEH//v2xefNmHDx4EOfOnUNcXBwA4G9/+xseffRR/PWvf0V4eHhbp0ZEROSTOtIxGvDdHWJuqQnaunUroqOj0a9fP2RkZKCkpER+LC8vD1arFWlpafJ9cXFxSE5Oxo4dOwAAubm50Gq1cgAEAMOHD4dWq3Uak5ycLAdAADB+/HiYzWbk5eU1e11msxkmk8npRkRE5MusNjtqrHW7utobBMknyftYJsjlQdDEiROxatUqbNmyBX/729+wZ88e3HnnnTCbzQAAvV4PtVqNiIgIp+fFxMRAr9fLY6Kjo5u8dnR0tNOYmJgYp8cjIiKgVqvlMVdatmyZXGOk1WqRkJDQ4fkSERF5soqahk7P7SmMBho1TPSxTFD7vhpX8cADD8i/T05Oxs0334zExERs3LgR06dPb/F5QghIkiT/ufHvOzKmscWLF2PBggXyn00mEwMhIiLyaY56oGB1AJQB7ct9+OrRGW7fIh8bG4vExEQcO3YMAKDT6WCxWGAwGJzGlZSUyJkdnU6H4uLiJq918eJFpzFXZnwMBgOsVmuTDJGDRqNBeHi4042IiMiXmTqwPd4h2EcPUXV7EFRaWopz584hNjYWAJCSkgKVSoXs7Gx5TFFREQoKCjBy5EgAwIgRI2A0GrF79255zK5du2A0Gp3GFBQUoKioSB6zefNmaDQapKSkuHtaREREXqGiA9vjHQLV3B0GAKioqMDx48flP586dQr5+fmIjIxEZGQklixZgvvvvx+xsbE4ffo0nn32WURFReG+++4DAGi1WsyaNQsLFy5E9+7dERkZiUWLFmHIkCHybrGBAwdiwoQJyMjIwMqVKwEAjz32GCZNmoT+/fsDANLS0jBo0CCkp6fj5ZdfxuXLl7Fo0SJkZGQww0NERFSvo9vjASBYxZogAMCPP/6IO+64Q/6zo8bmkUcewZtvvon9+/fjww8/RFlZGWJjY3HHHXfgk08+QVhYmPyc5cuXQ6lUYsaMGaiursZdd92FDz74AAEBDa25V61ahXnz5sm7yKZMmeLUmyggIAAbN27E7NmzMWrUKAQFBWHmzJl45ZVX2v5VICIi8lEd3R4P+O5J8m3+iowdOxZCiBYf/+abb675GoGBgXj99dfx+uuvtzgmMjISH3/88VVfp1evXvjqq6+u+X5ERET+yhXLYb4aBPHsMCIiIh8mL4dp2r8cJu8O87HlMAZBREREPszkguUweXcYM0FERETkLRzNEkM7shzmo2eHMQgiIiLyYa7YHRZUf4q8r+0OYxBERETkw1yyO4xnhxEREZG3aSiM7nhNEDNBRERE5DUatsi3fzkskGeHERERkbdpqAlyxdlhdpdck6dgEEREROTD5ANUXdAskZkgIiIi8gpCCNd0jPbRs8MYBBEREfmoSosNjpOuwju0Rb5hOcxub/noLG/DIIiIiMhHObbHqwIkaJTt/8h31AQBQE2t72SDGAQRERH5KLlbtEYJSZLa/TqByoYgyJd6BTEIIiIi8lEmF3SLBgCFQoI6oC5kqKn1nR1iDIKIiIh8lCu6RTtoVPVBkA8VRzMIIiIi8lHljZbDOsrRMNHsQ72CGAQRERH5KFd0i3YIdGSCWBhNREREns6xHBbuiuUwpWObPIMgIiIi8nDycpgLgiBHJojLYUREROTxXHFumINjm7yZy2FERETk6cpdtEUeaCiM9qVDVBkEERER+ShXbpEP5BZ5IiIi8haO3WGu2CLPwmgiIiLyGq6sCZKbJbJjNBEREXm6KktdEBSsZrPE5jAIIiIi8lGOIubGp8C3l2N3GJslEhERkcerrq/fCVJ1PAji2WFERETkNRzLYYEuCILkTBCXw4iIiMiT2e3Ctcthjo7RXA4jIiIiT2ZutIsryCVBEAujiYiIyAs4lsKAhqWsjmCzRCIiIvIKjqJojVIBhULq8OtpuDuMiIiIvIEjY+OKeiCgcSaIy2FERETkwaotdcGKK7bHA4BGxVPkiYiIyAvI2+NdlQniFnkiIiLyBtUuXg5js0QiIiLyCjUu7BYNMBNEREREXqLKUhcEuaJbdN3rsFkiEREReQFXnhsGsFkiEREReYlqi6u3yNe9jsVmh80uXPKaXY1BEBERkQ+Sa4JcVRitbAgZfGVJjEEQERGRD3J9TVDD6/hKcTSDICIiIh/k6i3yAQoJqoC64zeYCSIiIiKP5eot8oDvbZNnEEREROSDXL0cBjQcneErDRMZBBEREfmght1hSpe9pqM4mkEQEREReSy5T5DadR/1vnaSPIMgIiIiH+SWmiAfO0meQRAREZEPckdNUKCKhdFERETk4Rq2yLu+JoiZICIiIvJYNRb3LYexMJqIiIg8VpUbC6PNtVwOIyIiIg/l2CIf5MLlsIZmicwEERERkQey24WcrXHlcpiGhdFERETkyaobZWpcGgSxWSIRERF5ssZBkCNwcQVukSciIiKPVt1oZ5hCIbnsdRsKo/00E/T9999j8uTJiIuLgyRJ+Pzzz50eF0JgyZIliIuLQ1BQEMaOHYsDBw44jTGbzZg7dy6ioqIQEhKCKVOm4Pz5805jDAYD0tPTodVqodVqkZ6ejrKyMqcxZ8+exeTJkxESEoKoqCjMmzcPFoulrVMiIiLyKXK3aLXrlsIAZoJQWVmJoUOHYsWKFc0+/tJLL+HVV1/FihUrsGfPHuh0Otx9990oLy+Xx8yfPx/r169HZmYmcnJyUFFRgUmTJsFma4gsZ86cifz8fGRlZSErKwv5+flIT0+XH7fZbLj33ntRWVmJnJwcZGZmYu3atVi4cGFbp0RERORTqtzQIwhoVBPkI5kgiA4AINavXy//2W63C51OJ1588UX5vpqaGqHVasVbb70lhBCirKxMqFQqkZmZKY8pLCwUCoVCZGVlCSGEOHjwoAAgdu7cKY/Jzc0VAMThw4eFEEJs2rRJKBQKUVhYKI9Zs2aN0Gg0wmg0tur6jUajANDq8URERN4g98QlkfjMV+Kuv2116euu3nVGJD7zlZj1wW6Xvm5buerz26U1QadOnYJer0daWpp8n0ajwZgxY7Bjxw4AQF5eHqxWq9OYuLg4JCcny2Nyc3Oh1WqRmpoqjxk+fDi0Wq3TmOTkZMTFxcljxo8fD7PZjLy8vGavz2w2w2QyOd2IiIh8TbUbDk8F2CzxqvR6PQAgJibG6f6YmBj5Mb1eD7VajYiIiKuOiY6ObvL60dHRTmOufJ+IiAio1Wp5zJWWLVsm1xhptVokJCS0Y5ZERESerdpNy2FsltgKkuRciS6EaHLfla4c09z49oxpbPHixTAajfLt3LlzV70mIiIib+QIggJZGH1VLg2CdDodADTJxJSUlMhZG51OB4vFAoPBcNUxxcXFTV7/4sWLTmOufB+DwQCr1dokQ+Sg0WgQHh7udCMiIvI18gny7iqMZiaoqd69e0On0yE7O1u+z2KxYNu2bRg5ciQAICUlBSqVymlMUVERCgoK5DEjRoyA0WjE7t275TG7du2C0Wh0GlNQUICioiJ5zObNm6HRaJCSkuLKaREREXkVd22Rl4/N8JHdYW0+Va2iogLHjx+X/3zq1Cnk5+cjMjISvXr1wvz587F06VIkJSUhKSkJS5cuRXBwMGbOnAkA0Gq1mDVrFhYuXIju3bsjMjISixYtwpAhQzBu3DgAwMCBAzFhwgRkZGRg5cqVAIDHHnsMkyZNQv/+/QEAaWlpGDRoENLT0/Hyyy/j8uXLWLRoETIyMpjhISIiv+bYIh/orsJoH1kOa3MQ9OOPP+KOO+6Q/7xgwQIAwCOPPIIPPvgATz/9NKqrqzF79mwYDAakpqZi8+bNCAsLk5+zfPlyKJVKzJgxA9XV1bjrrrvwwQcfICCg4Zu1atUqzJs3T95FNmXKFKfeRAEBAdi4cSNmz56NUaNGISgoCDNnzsQrr7zS9q8CERGRD5GXw9xWE+QbmSBJCCG6+iK6islkglarhdFoZPaIiIh8xpINB/DBjtOYc0dfLBrf32WvW1hWjVEvboFaqcDRv0x02eu2las+v3l2GBERkY+Rt8i7uiaovjDaUmuH3e79ORQGQURERD7GsRzm+pqghtez2Ly/LohBEBERkY9xW02QsiFs8IW6IAZBREREPqbGTcdmKAMUUCqk+vdgJoiIiIg8jLu2yAO+1TCRQRAREZGPcRRGu3o5DGi0Td4HGiYyCCIiIvIx7uoYDTQEQb7QMJFBEBERkY+pctMp8gCgUXE5jIiIiDxUtTszQUrHchgzQURERORhqt20OwxgJoiIiIg8lM0uYKnP0rgjCJIzQQyCiIiIyJNUNwpO3FMYXX+SPJfDiIiIyJM4tsdLUkNPH1dq2B3GTBARERF5kMbdoiVJcvnrNzRLZCaIiIiIPIg7t8cDjZolMhNEREREnsSd2+OBRsthrAkiIiIiT1Lt5kwQt8gTERGRR6q21gJwYyZIybPDiIiIyANVW+qWqdxxgjzQOBPE5TAiIiLyII6aIHecIA+wWSIRERF5KHcemQGwMJqIiIg8VLWlvibIbUEQC6OJiIjIAzlqgty+RZ41QURERORJ3L0cJneM5u4wIiIi8iTychgzQdfEIIiIiMiHODJB7toiL9cEMRNEREREnqS6PkPjri3yGm6RJyIiIk/k7mMzAtkskYiIiDyRu4/NYCaIiIiIPJL7M0ENzRKFEG55j87CIIiIiMiHOGqC3Lc7rCF08Pau0QyCiIiIfIj7O0Y3vK63b5NnEERERORD3L1FXqmQoJDqfu/t2+QZBBEREfkQR02Qu7bIS5LkMw0TGQQRERH5EPnYDDcFQUBDlomZICIiIvIIVpsdVlvdji131QQBQKDSN06SZxBERETkIxoHJe7MBGkcmSAuhxEREZEncCyFKSRAHeC+j3gNM0FERETkSRo3SpQkyW3v07hhojdjEEREROQjOqMoGmh8fhgzQUREROQB5EyQ24Mg3zg/jEEQERGRj3D3uWEOck0Ql8OIiIjIE8jLYW4OghqaJTITRERERB6g02qClCyMJiIiIg/SWcthLIwmIiIij9J5u8NYGE1EREQepCETpHTr+zQ0S+RyGBEREXmAhkyQez/eNXKzRGaCiIiIyAN0Xk0Qzw4jIiIiD9J5W+RZGE1EREQepKFjtLtrguozQdwiT0RERJ6gIRPk3o93ZoKIiIjIo3Ta2WFslkhERESepGF3mHuXw3hsBhEREXkUFka3DYMgIiIiH9F5p8hzi3yzlixZAkmSnG46nU5+XAiBJUuWIC4uDkFBQRg7diwOHDjg9Bpmsxlz585FVFQUQkJCMGXKFJw/f95pjMFgQHp6OrRaLbRaLdLT01FWVubq6RAREXmNzmqW6MgEsVliMwYPHoyioiL5tn//fvmxl156Ca+++ipWrFiBPXv2QKfT4e6770Z5ebk8Zv78+Vi/fj0yMzORk5ODiooKTJo0CTZbwxd75syZyM/PR1ZWFrKyspCfn4/09HR3TIeIiMgrdNaxGb7SLNEtXyWlUumU/XEQQuC1117D//zP/2D69OkAgH/961+IiYnB6tWr8fjjj8NoNOLdd9/FRx99hHHjxgEAPv74YyQkJODbb7/F+PHjcejQIWRlZWHnzp1ITU0FALz99tsYMWIEjhw5gv79+7tjWkRERB6ts3aHaRw1QbU2CCEgSZJb389d3JIJOnbsGOLi4tC7d288+OCDOHnyJADg1KlT0Ov1SEtLk8dqNBqMGTMGO3bsAADk5eXBarU6jYmLi0NycrI8Jjc3F1qtVg6AAGD48OHQarXymOaYzWaYTCanGxERka/orMJoR02QEIDF5r3ZIJcHQampqfjwww/xzTff4O2334Zer8fIkSNRWloKvV4PAIiJiXF6TkxMjPyYXq+HWq1GRETEVcdER0c3ee/o6Gh5THOWLVsm1xBptVokJCR0aK5ERESewmqzo9YuAHRCn6BGzRi9eUnM5UHQxIkTcf/992PIkCEYN24cNm7cCKBu2cvhyrRZa1JpV45pbvy1Xmfx4sUwGo3y7dy5c62aExERkaerbrRd3d2ZIHWAAo6PW28ujnb7FvmQkBAMGTIEx44dk+uErszWlJSUyNkhnU4Hi8UCg8Fw1THFxcVN3uvixYtNskyNaTQahIeHO92IiIh8gaMeKEAhQRXg3hodSZIaukYzE9Qys9mMQ4cOITY2Fr1794ZOp0N2drb8uMViwbZt2zBy5EgAQEpKClQqldOYoqIiFBQUyGNGjBgBo9GI3bt3y2N27doFo9EojyEiIvInjXsEdUahsi80THT57rBFixZh8uTJ6NWrF0pKSvCXv/wFJpMJjzzyCCRJwvz587F06VIkJSUhKSkJS5cuRXBwMGbOnAkA0Gq1mDVrFhYuXIju3bsjMjISixYtkpfXAGDgwIGYMGECMjIysHLlSgDAY489hkmTJnFnGBER+aWGHkHuXQpzqCuOtnp1TZDLg6Dz58/joYcewqVLl9CjRw8MHz4cO3fuRGJiIgDg6aefRnV1NWbPng2DwYDU1FRs3rwZYWFh8mssX74cSqUSM2bMQHV1Ne666y588MEHCAho+MauWrUK8+bNk3eRTZkyBStWrHD1dIiIiLxCVSd1i3bwhYaJkhBCdPVFdBWTyQStVguj0cj6ICIi8mrbj1/Cw+/sQv+YMHzzu9vd/n4TXvseh/Xl+HhWKkYnRbn9/Rpz1ec3zw4jIiLyAZ3VKNFBI3eN9t5MEIMgIiIiH9BZjRIdApUNXaO9FYMgIiIiH9B1mSDvLYxmEEREROQDuioT5M2F0QyCiIiIfEBnb5H3hZPkGQQRERH5gK7aIs/CaCIiIupSNV3SLBEwMwgiIiKirlTdVZmgWi6HERERUReq6uTdYY6aIGaCiIiIqEvVdPbuMBZGExERkSfo/ANU2SyRiIiIPEBn1wTx2AwiIiLyCFVd1iyRy2FERETUhWq6qDCamSAiIiLqUuwY3XYMgoiIiHwAO0a3HYMgIiIiH9DZW+TljtGsCSIiIqKuIoSQl8OCO205rL4wmpkgIiIi6ipWm4DNLgAAgZ1dE8RMEBEREXUVR48goDO3yHN3GBEREXUxx1KYUiFBFdA5H+2aRoXRQohOeU9XYxBERETk5Tp7ezzQkAmyC6DWziCIiIiIukCVpRZA5y2FAQ2ZIMB7l8QYBBEREXm5mi7IBGmUCkiS4/29sziaQRAREZGXq7bUBSGdmQmSJKnhJHlmgoiIiMidik01sDSzJb0raoKAxg0TGQQRERGRG5wprUTGhz8idel/8MzafU0e74qaIKDx0RlcDiMiIiIXqjDX4n+zDuPuV79H9sFiAMCXP1/A5UqL07jOPjLDwdEwkZkgIiIicgm7XWBt3nnc+cpWvLn1BCw2O0b3jULf6FDU2gU27rvgNN7RLLGzl8MaGiYyE0REREQdVFhWjfve3IGFn/6MknIzErsH4+1f3YyPZt2KB29JAAB8nu8cBFV1USZI4+UnyTMIIiIi8iAvZx3Gz+fKEKIOwB8mDsDm392OuwfFQJIkTB4aB0kC8s4YcO5ylfycGmaC2oVBEBERkYew2QW2Hb0IAHjnkVvwxJg+8g4sAIgJD8TIPt0BAF/kF8r3d9nuMMdJ8qwJIiIioo7YX2iEocqKsEAlbrkuotkxU2/sCaBuScxxZleVpWsLo5kJIiIiog7ZeqQEADC6bxSULRyEOiFZB7VSgeMlFThwwQSgUSaoy4IgZoKIiIioAxxLYWP69WhxTHigCuMGRgNoWBLrimMzADR0jOZyGBEREbWXodKCn8+VAQDG9G85CAIalsQ2/HwBNrto2CLfRc0SzVwOIyIiovbKOX4JdgH0jwlDrDboqmPH9u+B8EAlik1m7DpZ2lAT1FW7w5gJIiIiovbaeqR+KewaWSCg7syue2+IBQB8kX+h6ztGMxNERERE7WFvtDX+avVAjTmWxDYVFKGs2gqgCzJBXt4sUdnVF0BEROTvDulNuFRhRrA6ADe3sDX+SrdeF4k4bSAuGGtQXtM1B6hqlNwdRkRERB3gyAKN7NPdqTni1SgUEibfGOd0X1dlgsy1XA4jIiKidpDrgVq5FOYwrX5JzKHzzw5jJoiIiIjayVRjxU9nDACAMf2i2/TcAbow9IsJlf/c+ZkgdowmIiKidtpxvBS1doHro0LQq3twm54rSZJcIA10RU0QmyUSERFROznqgW5v41KYw9Qb4xCgkBCmUXKLfBtxdxgREVEXEULg+6Ot7w/UnPiIYHw8KxWqAKnF88bcJdDLM0EMgoiIiLrI8ZIKFJZVQ6NUYMT13dv9OiP6tP+5HeHtmSAuhxEREXURx1JY6vXd5YDCm/AUeSIiIgDGKit+9d5uTPvHduw6WdrVl+MV2tol2tPIhdEMgohcx24XWLLhAJ74KE8+VZmIPNflSgseensnvj96EfnnyvDAP3fid5/ko6S8pqsvzWNVWWqx6+RlAN4bBMnLYWyWSOQ67+acwgc7TiPrgB5T/7Eds1fl4cTFiq6+LKIOc/dPzEII5J8rQ4W51q3v09jFcjMe/GcuDhaZEBWqxn+lxEOSgPV7C3HXK9vwrx2nYbOLTrseb7HzZCksNjviI4LQp0dIV19Ouzg6RtfaBWpt3hcIMQgij/PzuTL8b9ZhAMCtvSMhScCm/XqkLf8ei9ftg97InyzJPYRw7wf1pv1FuPFPm7Ho05/d8l55ZwyY/uYOTPvHdsx4K7dTlij0xho88M9cHC2uQHSYBpmPjcArvxiK9bNHYUhPLcrNtXh+wwFMWZGDn84a3H493mRboy7RkiR18dW0T+M6phovzAZxdxh5FFONFXPX7EWtXeCeITr8Y+YwHCkuxyvfHMG3h0qwZvc5rPupEDNuTkBEiBpCCNiFgF0AdiEgQcIN8VqM6hsFbZCqq6dDXuSrfRfw/BcH8OCtCViU1t/lH0oFhUYs+Hc+aqx2fJZ3Hn16hOK3Y/u45LXPXa7Ci1mHsXFfkXzfwSIT/jfrMJ6fPNgl79GcwrJqzHx7J86UViFOG4jVGcNxXVRdRuPGhG74/MlRWL37LF7OOowDF0yY/sYOPHhLAp6ZMAARIWqXX09BoRHfHirGnQOiMaSn1qMDi1OXKvF5/gUA3rsUBgDqRlvya6w2hGq8K6yQhLt/9PFgJpMJWq0WRqMR4eHhXX05fk8Igblr9uKrfUXo2S0Im566zSmQ2XP6Mv7368P48cy1f5oMUEgY1qsbxvTrgTH9ojE4LhwKhef+h+jvhBA4U1qFH45fQqW5Fg/UB7md5ZsDesxe9ZO8ZPM/9wxExu3Xu+z1L1WYMeX1HFww1iCxezDOlFZBkoAPfn1rhz4ATTVW/GPLcby//TQsNjskCZiRkoCbr4vA7z/bBwB479GbceeAGFdNRXa2tAoPvb0ThWXVSIgMwur/Ho6EyOa7HV+qMGPZpsNY+9N5AEC3YBWemTAAD9yc4LJ/l5/sOYs/fn4AlvolmaToUNyfEo/7buqJmPBAl7yHq1yutGD6G9txurQKQxO64bMnRkDVyf19XKnfc1/DUmvH9j/ciZ7dgjrlPV31+c0giEFQpzDX2rDnlAFDE7QIC2w+Q/PJnrN4Zu1+BCgkfPrECAzrFdFkjBAC3x0pwdYjFyEEoJDq2sYrJAkKCai22rDzZClOXKx0el73EDVmpvbCU3clua2ZWI3VBqWi85uVudLZ0ios+/oQbHaBuXcmYUi81m3vZai0YPuJS9h+/BJ+OHYJ5w3V8mM9uwXhzV8Oww3x3dz2/g5bj5TgsQ/zYLHZMTA2HIeKTACAvz94o9NxBO1lqbXj4Xd2Ys9pA3pHheDz2aOw7OtDyNxzDtogFb6cM7rNRyVYbXas2X0Wr317DJcrLQCA0X2j8Ow9AzEoru7/she+PID3t59GZIgaWU/dhmgXBgKH9SY8+t4e6E016B0VgtUZqYjVXvvDb8/py/jj5wU4rC8HUJct+su0ZCT3bP/fM3OtDUs2HMSa3WcBAANjw3HyYoVcqKuQgNuSeuD+lHikDYrp8m3oNVYbHn5nF/LOGBAfEYT1s0ehR5imS6+po25Y8g1MNbX4z8Ix6NMj9NpPcAEGQS7AIKhzHCsux9w1e3FYXw5tkAq/HnUdHh15HboFq53GTF6RgxqrHc9MGNDhZYJzl6vw/bGL2HbkIrYfv4RKS11txPDrI7Fi5jBEhbruPx27XWDVrjP436wjCA9U4s/TknHXQNf/5O1OVpsd7/xwCn//z1GngxAnDNZhQVo/9IsJc9l77TpZir//5xhyT5ai8f8+qgAJw3pFoMhYg7OXq6AOUOCPkwfhl6m93LaskXuiFI++vxvmWjvuHRKLvz94I5ZuOoz3tp+CKkDCv359K0b2jWr36wsh8Oz6AqzZfRZhGiXWPzkKfaNDYa614YGVO5F/rgwDdGFYN3skgtXXXkYQQmDL4RIs3XRIDvT7Rofif+4ZiLH9netKzLU2TPvHDhwqMmF03yh8+JtbXZJ12XK4GHNX70WlxYak6FCs+u/UNgVYtTY7PthxGsuzj6LSYoNCAtKHJ2JBWv82L2EXGavxxMc/4edzZZAkYOHd/TB7bF9UWGqxcV8R1uadd8ochwUqMemGWNw/LB4piRGdvlxmtwvMy6zLdocHKrFu9kj0jXbdv62ucutfv0VJuRkb543G4Dj3/eDUGIOgem+88QZefvllFBUVYfDgwXjttddw2223teq5DILcSwiBj3eewV82HoK51g6FBDg2iIRqlEgfkYhZo3sjVKPE1BXbcaS4HLclReFfv3bNf9YOllo7vi4owrPr9qPSYkOsNhBvPDwMNzWTaWqrU5cq8czafdh96rLT/fcOicXzkwe59KfvI/py/PP7kzh3uW4pRSFJUCgACRIkqa5AsWe3IMRHBNX/GoyeEUGICFZd9T/7vWcNWLxuv/zT+cg+3aELD8T6/EIIAUgSMO3Gnpg/LgmJ3du/g2XvWQNezT6KH45dku/rFxOK0X174LakKNzaOxIhGiVMNVYs+vfP2HywGAAw7cY4LJ0+pFVBQlvknTEg/d1dqLLYMG5gNN54OAVqpQJ2u8DczL3YuK8IYRolPnl8hJxdaauPck/jj18cgCQB7z1yC+4Y0HBCuN5Yg0mv5+BShRmTh8bh/x688arfp4MXTPjrpoPYfryu/05kiBq/u7sfHrolocXs4/GSckx6ve6Hi8UTB+DxMe3/4UIIgfe2n8ZfNx6EXQAjru+ON385zOmHmbYoNtXgLxsP4cuf6+piokI1ePaeAbjvpp6tCk5yT5Ri7pqfcKnCAm2QCn9/8EaM7d/0BPbTlyqx7qfzWPtTIQrLGrKN13UPxvRh8Zg+rCfiI9qWiWuvl785jH98d6IuwP7NrRjZp/0Btie5/aXvcPZyFdb+diRSEjv+/2prMAgC8MknnyA9PR1vvPEGRo0ahZUrV+Kdd97BwYMH0atXr2s+n0FQyyy1dmw/cQlbD5dAFaBAr+7BSIgIRkJk3YfrtVLKlyrMePqzfdhyuARA3cGAL//XDfjxtAGvbzkmf+AGqhToHxOGn88bERWqwddP3ea21PDxknI89lEeTl6shDpAgSVTBuOhWxNa/A9XCNHiY7U2O97NOYVXs4/CXGtHsDoAvx/fH3pjDd7JOQWbXSAsUInFEwfiwVs6VvdwRF+O//vPMWzcX3Ttwc0IUQegT3QokqLD0C8mFP1iwpAUE4rwIBX+9s0RfLjzDIQAIoJVeO7eQZg+rO5D6FhxOV7NPoqvC/QAAKVCwrSbemJU3+4Y0lOL3lGhCGjFvAoKjViefRT/qf+7oFRIeOCWBPx2bJ8WP3yEEHjnh1N4MeswbHaBpOhQvPnLFPSNrku1W212GCotuFxlweVKC5QKBcKDlAgLVCE8UIkQtfKqX/P9542Y+fZOlJtrcVtSFN7+1c3Ou1ysNvzqvd3YfeoyYsI1WDd7VJtrHXJPlCL93V2otQv8YeIAPNFMALL71GXMfHsnau2i2TqkaosNJy5W4MPc0/g07zyEqCtE/c3o3ph9Rx+Et7C03Nia3WexeN1+KBUS1v52JIYmdGvTPIC6r/fzGw5g9a66JacHb0nAn6clu6SOZfvxS/jjFwU4WZ/ZurV3JP48NRn9dc1nSE5crMC6n87jrW0nYbMLDIwNx8pfplxzSdFuF9h5qhRr8wrxdUERqiwNO+eGXx+J+4fF454hsQhxU2Hvv/ecw9Nr6+q0XvnFUPxXSrxb3qcr3P3qNhwrqcDq/07tUOa0LRgEAUhNTcWwYcPw5ptvyvcNHDgQ06ZNw7Jly675fAZBziy1duQcv4iN+/TIPqiHqablPiPRYRpc1z0EvaNCcH0Px6+h6BUZjO0nLuH3n/6MSxUWqJUKLJ44AI+MuE7+UBJC4D+HSvD6d8flRoiSBHz0m1SMTnLvP6DyGisWffozvjlQl2WYcXM8/jQ1GRfLzThwwYSDRSYcKjLh4AUT9KYaxEcEoW+PUPSJDkWfHiHo0yMUCoWEJRsOYN95IwDgtqQoLL1viFwUWlBoxOJ1+7G/sO7xW66LwP+bNBh9o0MRpG59PUJzwc89Q3SYmBwLqT6rJoSAqN8ZV2muRWFZDc4bqlBYVo3zhmpcLDe3+PqSBHk5avqwnnju3kGIbKYYef95I17ZfETubOsQrA5AcpwWyT21GBAbBrtdoMJci/KauluF2YoiY42c+VFIwPRh8XjqrqQWC2ivtPvUZcxZ/RNKys0IUgUgJlyD0koLyq/yd9PxXqEaJcKDVAgPVCEs0Pn3n+cXoqzKiluvi8QHv7ml2SyTscqKX6zcgaPFFegbHYrMx4ZDrVSgxmqD2WpHtdWGGqsNNU6/r7tVW2z4+3+OwVBlxbQb47D8gZazPB/mnsb/++IAFBKwaHx/GKutOF5cgWMlFThnqHJaMpw8NA5Pj+/f6q8fUPd35MnVP2HTfj0Suwdj47zb2rSDx1hlxezVedh+vBSSVFc0Pmt0b5cuJVlq7Xgn5yRe/89xVNfX1v161HV4alw/hKgDcKykApv2F+Hr/XocKS6Xnzftxjgsm35Dm/5dAUCluRZZBXqs/em807JskCoAE4fo8F/D4jH8+u4uyUiba23IOXYJj3+Uh1q7wLw7+2JBWv8Ov64nmfx6DvYXGvHnqYMxeWgctEFXzz67gt8HQRaLBcHBwfj0009x3333yfc/9dRTyM/Px7Zt25o8x2w2w2xu+FAwmUxISEhweRC082Qp1uadd9nruZq8jCJJCKgvKC6rtmLL4RKnD5ceYRqMHxyDQGUAzl6uwjlDNc6WVsr1Nc0JUEjyDpv+MWH4+0M3YoCu+a+tEAI5xy9h9a6zGNU3Cr8cnujaibZACIG3tp3Ey98chl3UZSZq29HILTxQiecmDcIvUuKb/IO32QX+teM0Xtl8xOknzlCNElGhavQI06BHmAbdgtWQ0BDQ2IWAzQ6UVpqx9UhD0HHPEB3m3ZXU4teyJTVWG84bqnCsuAJHiytwtKQcx4rLcepSJaw2geu6B+Ov9w3BqFb89Lbn9GVs3FeEgkIjDlwwobqVPWgkCZh8QxyeGpfUrqLJkvIaPLUmH7lXHMMgSUBEsBrdglUQAjBVW2GqscJqa933cmhCN3w869YWC/UB4EJZNaa/sQN6U/t6U90Qr8W/Hx9x1cypEAK//2wfPmvh/4xuwSoMje+Gp8YlNbtZoDWMVVbc838/oLCsGkPjtbi+R10WT6mQ5F8df4ft9YG1QN2vO06U4tSlSgSrA/B/D96EcYPcV+9WWFaNP315QP4hRRceiBBNgNNGB6VCwqi+Ubg/JR6Tb4jt8IdtYVk11v90Hp/lncfp0ir5/p7dgnB7vyioAhRwvEPj9xJCQAByiw4hBGx2gbIqK0orLSitMKO0woLyRk0rrxUQe6sHVuZiV6OSgBB1AHpG1C/JdwvCiD7dcc+QWJe+p98HQRcuXEDPnj2xfft2jBw5Ur5/6dKl+Ne//oUjR440ec6SJUvwwgsvNLnf1UHQ6l1n8ez6/S57vc4UHabBxGQd7hkSi5uvi2yy3CGEgKHKirOXq3CmtBInLlbi5MUKnLpUiVOXKuUP/EdHXoc/TBzQ5Tsxribn2CXMXfMTDFVWqAIk9IsJw6DYcAyKC8eg2HD0jAjCucvVOH6xAidKKnCi/tficjPuHhiDP00dfM2aH8d/6luPXGxXW/n2Bj/XYrXZoTfWQKcNbNeShs0ucOJiBfafN2J/oREnLlZAo1QgVKNEaGDdslSoRomwQCVGXN8dSR0srLbZhdxoLyJYjcgQNbRBqmb/fppr7fUBUS1MNVaYqq0ol39fi/IaK0I0SvxyeGKrCnGP6Mvx6Pu7UVTfpFOS6jIGgaoABCoVCFQHIFAZgECVAkHy7wMQEx6IJ8Zej+iwa9eF1Vht+MPafSgpNyMpOhR9Y8Lqfo0ORfcQtUs+NPecvowHVuaiPY2b47SBeOeRW9pdG9VW3x0uwfMbDuDs5bqgRB2gwO39ojAhORZ3D4yBNtj1PcCEqPs79lleIb7ad+Ga2ca2UCokjB+sw6sPDIVG6bn/J7bXlsPF+L//HMd5QzUuVTTNPj90ay8smz7Epe/JIKg+CNqxYwdGjBgh3//Xv/4VH330EQ4fPtzkOZ2VCTpwwYjvj1669sAu4Pjpzm5vaDAohECAQoGRfbsjpVdEu1PAQggUm+q+vjqtZ/XlaEmVpRaFhmokdg+BWtm6YMBuF23+GglRt1R0qcKCi+VmXCw341KFGZcrLQ1FzvXb/R0/md+W1KPFugjqXLU2OyotNgSqFFAHKLz2J/mCQiN2n7oMuxCotddlLmptAja7Hbb6ZqMKCYAkof4XhKiVmHZTz07fxl1jteHLny9ArVTgzgHRV83YueO9vz1UjKP1tYuOD0nHp6WAgEKqy55JQP3v65ZhtcFqRIWo0T1Ug+6hakSFaBAepPTavzNtVWO1obCsGoWG6vpl+SrcmBCBu12cQXRVEORdrR0biYqKQkBAAPR6vdP9JSUliIlp/out0Wig0bj/H/LgOG2nbRP0JJIkeU3w4xCsVrY5S9GeIFGSJIQFqhAWqELvKO88I8ifKQMU0AZ5b/8nh+Se2g715OlMgaoA/OLmhC5770k3xAE3dMnbe7VAVQD69AjttH5BHeW1/6rVajVSUlKQnZ3tdH92drbT8hgRERFRc7w2EwQACxYsQHp6Om6++WaMGDEC//znP3H27Fk88cQTXX1pRERE5OG8Ogh64IEHUFpaij/96U8oKipCcnIyNm3ahMTEztllRERERN7LawujXYF9goiIiLyPqz6/vbYmiIiIiKgjGAQRERGRX2IQRERERH6JQRARERH5JQZBRERE5JcYBBEREZFfYhBEREREfolBEBEREfklBkFERETkl7z62IyOcjTLNplMXXwlRERE1FqOz+2OHnrh10FQeXk5ACAhIaGLr4SIiIjaqry8HFqttt3P9+uzw+x2Oy5cuICwsDBIktTVl9NmJpMJCQkJOHfunF+dfeav8wY4d3+cu7/OG/DfufvrvIHWz10IgfLycsTFxUGhaH9lj19nghQKBeLj47v6MjosPDzc7/6hAP47b4Bz98e5++u8Af+du7/OG2jd3DuSAXJgYTQRERH5JQZBRERE5JcYBHkxjUaD559/HhqNpqsvpVP567wBzt0f5+6v8wb8d+7+Om+g8+fu14XRRERE5L+YCSIiIiK/xCCIiIiI/BKDICIiIvJLDIKIiIjILzEI6mLff/89Jk+ejLi4OEiShM8//9zp8eLiYjz66KOIi4tDcHAwJkyYgGPHjjmNefzxx9GnTx8EBQWhR48emDp1Kg4fPuw0xmAwID09HVqtFlqtFunp6SgrK3Pz7Frmink7CCEwceLEZl/H0+YNuGbuY8eOhSRJTrcHH3zQaYynzd1V3/Pc3FzceeedCAkJQbdu3TB27FhUV1fLj3vavIGOz/306dNNvt+O26effiqP88W5A4Ber0d6ejp0Oh1CQkIwbNgwfPbZZ05jPG3urpj3iRMncN9996FHjx4IDw/HjBkzUFxc7DTG0+a9bNky3HLLLQgLC0N0dDSmTZuGI0eOOI0RQmDJkiWIi4tDUFAQxo4diwMHDjiNMZvNmDt3LqKiohASEoIpU6bg/PnzTmNcMXcGQV2ssrISQ4cOxYoVK5o8JoTAtGnTcPLkSXzxxRfYu3cvEhMTMW7cOFRWVsrjUlJS8P777+PQoUP45ptvIIRAWloabDabPGbmzJnIz89HVlYWsrKykJ+fj/T09E6ZY3NcMW+H1157rcVjTzxt3oDr5p6RkYGioiL5tnLlSqfHPW3urph3bm4uJkyYgLS0NOzevRt79uzBnDlznNrme9q8gY7PPSEhwel7XVRUhBdeeAEhISGYOHGi/Fq+OHcASE9Px5EjR7Bhwwbs378f06dPxwMPPIC9e/fKYzxt7h2dd2VlJdLS0iBJErZs2YLt27fDYrFg8uTJsNvt8mt52ry3bduGJ598Ejt37kR2djZqa2uRlpbm9P186aWX8Oqrr2LFihXYs2cPdDod7r77bvk8TwCYP38+1q9fj8zMTOTk5KCiogKTJk1y/eeaII8BQKxfv17+85EjRwQAUVBQIN9XW1srIiMjxdtvv93i6/z8888CgDh+/LgQQoiDBw8KAGLnzp3ymNzcXAFAHD582PUTaaOOzDs/P1/Ex8eLoqKiJq/j6fMWov1zHzNmjHjqqadafF1Pn3t7552amiqee+65Fl/X0+cthOv+nd94443iN7/5jfxnX557SEiI+PDDD51eKzIyUrzzzjtCCM+fe3vm/c033wiFQiGMRqM85vLlywKAyM7OFkJ4/ryFEKKkpEQAENu2bRNCCGG324VOpxMvvviiPKampkZotVrx1ltvCSGEKCsrEyqVSmRmZspjCgsLhUKhEFlZWUII182dmSAPZjabAQCBgYHyfQEBAVCr1cjJyWn2OZWVlXj//ffRu3dvJCQkAKj76Vmr1SI1NVUeN3z4cGi1WuzYscONM2if1s67qqoKDz30EFasWAGdTtfkdbxt3kDbvuerVq1CVFQUBg8ejEWLFjn9FOVtc2/NvEtKSrBr1y5ER0dj5MiRiImJwZgxY5y+Lt42b6B9/87z8vKQn5+PWbNmyff58txHjx6NTz75BJcvX4bdbkdmZibMZjPGjh0LwPvm3pp5m81mSJLk1DQwMDAQCoVCHuMN8zYajQCAyMhIAMCpU6eg1+uRlpYmj9FoNBgzZox8zXl5ebBarU5j4uLikJycLI9x1dwZBHmwAQMGIDExEYsXL4bBYIDFYsGLL74IvV6PoqIip7FvvPEGQkNDERoaiqysLGRnZ0OtVgOoW0+Pjo5u8vrR0dHQ6/WdMpe2aO28f/e732HkyJGYOnVqs6/jbfMGWj/3hx9+GGvWrMHWrVvxxz/+EWvXrsX06dPlx71t7q2Z98mTJwEAS5YsQUZGBrKysjBs2DDcddddci2Ft80baNu/c4d3330XAwcOxMiRI+X7fHnun3zyCWpra9G9e3doNBo8/vjjWL9+Pfr06QPA++bemnkPHz4cISEheOaZZ1BVVYXKykr8/ve/h91ul8d4+ryFEFiwYAFGjx6N5ORkAJCvKyYmxmlsTEyM/Jher4darUZERMRVx7hi7gyCPJhKpcLatWtx9OhRREZGIjg4GFu3bsXEiRMREBDgNPbhhx/G3r17sW3bNiQlJWHGjBmoqamRH2+uZkYI0WItTVdqzbw3bNiALVu24LXXXrvqa3nTvIHWf88zMjIwbtw4JCcn48EHH8Rnn32Gb7/9Fj/99JM8xpvm3pp5O+ogHn/8cfz617/GTTfdhOXLl6N///5477335NfypnkDbft3DgDV1dVYvXq1UxbIwVfn/txzz8FgMODbb7/Fjz/+iAULFuAXv/gF9u/fL4/xprm3Zt49evTAp59+ii+//BKhoaHQarUwGo0YNmyY09fGk+c9Z84c7Nu3D2vWrGny2JXX15prvnKMK+aubPVI6hIpKSnIz8+H0WiExWJBjx49kJqaiptvvtlpnKM6PikpCcOHD0dERATWr1+Phx56CDqdrsmOAgC4ePFik2jcU1xr3lu2bMGJEyfQrVs3p+fdf//9uO2227B161avnDfQ+u95Y8OGDYNKpcKxY8cwbNgwr5z7teYdGxsLABg0aJDT8wYOHIizZ88CgFfOG2jb9/yzzz5DVVUVfvWrXznd76tzP3HiBFasWIGCggIMHjwYADB06FD88MMP+Mc//oG33nrLK+femu95WloaTpw4gUuXLkGpVKJbt27Q6XTo3bs3AM/+ns+dOxcbNmzA999/j/j4ePl+R+mCXq+X/00DdcvdjmvW6XSwWCwwGAxO2aCSkhI5++mquTMT5CW0Wi169OiBY8eO4ccff2xxCchBCCGvO48YMQJGoxG7d++WH9+1axeMRqNTOt0TtTTvP/zhD9i3bx/y8/PlGwAsX74c77//PgDvnjfQtu/5gQMHYLVa5f9UvHnuLc37uuuuQ1xcXJPttkePHkViYiIA75430Lrv+bvvvospU6agR48eTvf76tyrqqoAwGkHIFBXQ+PIDnrz3FvzPY+KikK3bt2wZcsWlJSUYMqUKQA8c95CCMyZMwfr1q3Dli1b5IDNoXfv3tDpdMjOzpbvs1gs2LZtm3zNKSkpUKlUTmOKiopQUFAgj3HZ3Nta6U2uVV5eLvbu3Sv27t0rAIhXX31V7N27V5w5c0YIIcS///1v8d1334kTJ06Izz//XCQmJorp06fLzz9x4oRYunSp+PHHH8WZM2fEjh07xNSpU0VkZKQoLi6Wx02YMEHccMMNIjc3V+Tm5oohQ4aISZMmdfp8HTo67+bgih0YQnjevIXo+NyPHz8uXnjhBbFnzx5x6tQpsXHjRjFgwABx0003idraWnmcp83dFd/z5cuXi/DwcPHpp5+KY8eOieeee04EBgbKOyGF8Lx5C+G6v+/Hjh0TkiSJr7/+utn38cW5WywW0bdvX3HbbbeJXbt2iePHj4tXXnlFSJIkNm7cKI/ztLm74nv+3nvvidzcXHH8+HHx0UcficjISLFgwQKnMZ4279/+9rdCq9WKrVu3iqKiIvlWVVUlj3nxxReFVqsV69atE/v37xcPPfSQiI2NFSaTSR7zxBNPiPj4ePHtt9+Kn376Sdx5551i6NChLv8/jkFQF/vuu+8EgCa3Rx55RAghxN///ncRHx8vVCqV6NWrl3juueeE2WyWn19YWCgmTpwooqOjhUqlEvHx8WLmzJlNtgiWlpaKhx9+WISFhYmwsDDx8MMPC4PB0IkzddbReTenuSDI0+YtRMfnfvbsWXH77beLyMhIoVarRZ8+fcS8efNEaWmp0/t42txd9T1ftmyZiI+PF8HBwWLEiBHihx9+cHrc0+YthOvmvnjxYhEfHy9sNluz7+Orcz969KiYPn26iI6OFsHBweKGG25osmXe0+buink/88wzIiYmRqhUKpGUlCT+9re/Cbvd7jTG0+bd3JwBiPfff18eY7fbxfPPPy90Op3QaDTi9ttvF/v373d6nerqajFnzhwRGRkpgoKCxKRJk8TZs2edxrhi7lL9RRMRERH5FdYEERERkV9iEERERER+iUEQERER+SUGQUREROSXGAQRERGRX2IQRERERH6JQRARERH5JQZBRERE5JcYBBEREZFfYhBEREREfolBEBEREfklBkFERETkl/4/yjJ2rUsPBrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorted(list(data['release_year'].unique())),data.groupby('release_year')['user_id'].count().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "407aa338",
   "metadata": {},
   "outputs": [],
   "source": [
    "lbe = LabelEncoder()\n",
    "new_data['user_id'] = lbe.fit_transform(new_data['user_id'])\n",
    "new_data['movie_id'] = lbe.fit_transform(new_data['movie_id'])\n",
    "# data['user_genre1'] = enc.transform(np.array(data['user_genre1']).reshape(-1,1))\n",
    "\n",
    "# train_data = data[['user_id', 'movie_id','user_genre1','user_avg_rating','user_rating_stddev']]\n",
    "train_data = new_data[['user_id', 'movie_id']]\n",
    "train_data['label'] = new_data['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a422101",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['user_id'] = lbe.fit_transform(test_data['user_id'])\n",
    "test_data['movie_id'] = lbe.fit_transform(test_data['movie_id'])\n",
    "test_data['user_genre1'] = enc.transform(np.array(test_data['user_genre1']).reshape(-1,1))\n",
    "test_dataset = test_data[['user_id', 'movie_id','user_genre1','user_avg_rating','user_rating_stddev']]\n",
    "test_dataset['label'] = test_data['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "86cd2eb9",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>release_year</th>\n",
       "      <th>user_avg_rating</th>\n",
       "      <th>user_rating_stddev</th>\n",
       "      <th>user_genre1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>11096</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.86</td>\n",
       "      <td>0.74</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>18410</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.48</td>\n",
       "      <td>1.28</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>21222</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>12598</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2.97</td>\n",
       "      <td>1.48</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>14326</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>3.60</td>\n",
       "      <td>0.72</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88822</th>\n",
       "      <td>894</td>\n",
       "      <td>19066</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1968</td>\n",
       "      <td>3.35</td>\n",
       "      <td>0.85</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88823</th>\n",
       "      <td>894</td>\n",
       "      <td>6048</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1968</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88824</th>\n",
       "      <td>895</td>\n",
       "      <td>11905</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>3.53</td>\n",
       "      <td>0.82</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88825</th>\n",
       "      <td>895</td>\n",
       "      <td>18796</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1951</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.42</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88826</th>\n",
       "      <td>896</td>\n",
       "      <td>2192</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1953</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.89</td>\n",
       "      <td>(0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88827 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movie_id  user_id  rating  release_year  user_avg_rating  \\\n",
       "0             0    11096     3.0          1995             3.86   \n",
       "1             0    18410     3.5          1995             3.48   \n",
       "2             0    21222     3.0          1995             3.00   \n",
       "3             9    12598     0.5          1995             2.97   \n",
       "4           101    14326     4.0          1996             3.60   \n",
       "...         ...      ...     ...           ...              ...   \n",
       "88822       894    19066     3.0          1968             3.35   \n",
       "88823       894     6048     2.0          1968             2.00   \n",
       "88824       895    11905     5.0          1951             3.53   \n",
       "88825       895    18796     2.0          1951             2.73   \n",
       "88826       896     2192     2.0          1953             3.67   \n",
       "\n",
       "       user_rating_stddev                                        user_genre1  \n",
       "0                    0.74    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "1                    1.28    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "2                    0.00    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "3                    1.48    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "4                    0.72    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "...                   ...                                                ...  \n",
       "88822                0.85    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "88823                1.00    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "88824                0.82    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "88825                1.42    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "88826                0.89    (0, 7)\\t1.0\\n  (1, 0)\\t1.0\\n  (2, 0)\\t1.0\\n ...  \n",
       "\n",
       "[88827 rows x 7 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59add423",
   "metadata": {},
   "source": [
    "### 使用具名元组为特征进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f3780d2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "SparseFeat = namedtuple('SparseFeat', ['name', 'vocabulary_size', 'embedding_dim'])\n",
    "DenseFeat = namedtuple('DenseFeat', ['name', 'dimension'])\n",
    "\n",
    "dnn_features_columns = [SparseFeat('user_id', train_data['user_id'].nunique(), 8),\n",
    "                        SparseFeat('movie_id', train_data['movie_id'].nunique(), 8),\n",
    "#                         SparseFeat('user_genre1', 19, 4),\n",
    "#                         DenseFeat('user_avg_rating', 1),\n",
    "#                         DenseFeat('user_rating_stddev', 1),\n",
    "                       ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d15a85",
   "metadata": {},
   "source": [
    "### 搭建模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8e1a9f",
   "metadata": {},
   "source": [
    "#### 1 输入层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "68509499",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_input_layers(dnn_features_columns):\n",
    "    dense_input_dict, sparse_input_dict = {}, {}\n",
    "    \n",
    "    for f in dnn_features_columns:\n",
    "        if isinstance(f, SparseFeat):\n",
    "            sparse_input_dict[f.name] = Input(shape=(1), name=f.name)\n",
    "        elif isinstance(f, DenseFeat):\n",
    "            dense_input_dict[f.name] = Input(shape=(f.dimension), name=f.name)\n",
    "    \n",
    "    return dense_input_dict, sparse_input_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b288c5b",
   "metadata": {},
   "source": [
    "#### 2 Embedding层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "89c38811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_embedding_layers(dnn_features_columns, sparse_input_dict, prefix=\"\", is_linear=True):\n",
    "    embedding_layers_dict = {}\n",
    "    \n",
    "    sparse_feature_columns = list(filter(lambda x: isinstance(x, SparseFeat), dnn_features_columns)) if dnn_features_columns else []\n",
    "    \n",
    "    if is_linear:\n",
    "        for f in sparse_feature_columns:\n",
    "            embedding_layers_dict[f.name] = Embedding(f.vocabulary_size + 1, 1, name= prefix + '_1d_emb_' +  + f.name)\n",
    "    else:\n",
    "        for f in sparse_feature_columns:\n",
    "            embedding_layers_dict[f.name] = Embedding(f.vocabulary_size + 1, f.embedding_dim, name=prefix + '_kd_emb_' +  f.name)\n",
    "    \n",
    "    return embedding_layers_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0682d0c0",
   "metadata": {},
   "source": [
    "#### 3 GML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "748b5f08",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_gml_layers(gml_user_embedding, gml_movie_embedding):\n",
    "    \n",
    "    return Multiply()([gml_user_embedding, gml_movie_embedding])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34eb471f",
   "metadata": {},
   "source": [
    "#### 4 MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69565ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_mlp_layers(mlp_input, units=(32, 16)):\n",
    "    for out_dim in units:\n",
    "        mlp_input = Dense(out_dim)(mlp_input)\n",
    "    \n",
    "    return mlp_input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0376caa",
   "metadata": {},
   "source": [
    "#### 5 输出层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9329b444",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bulid_output_layers(concat_output):\n",
    "    return Dense(1)(concat_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e36a60e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NCF(dnn_features_columns):\n",
    "    # 1. 获取字典输入层，键为列名，值为对应的Input\n",
    "    _, sparse_input_dict = build_input_layers(dnn_features_columns)\n",
    "    \n",
    "    # 2. 获取真实输入层，使用列表存储每个列的Input\n",
    "    input_layers = list(sparse_input_dict.values())\n",
    "    \n",
    "    # 3. 将SparseFeature进行Embedding，有两路，分别是GML和MLP\n",
    "    embedding_gml_dict = build_embedding_layers(dnn_features_columns, sparse_input_dict, prefix=\"GML\", is_linear=False)\n",
    "    embedding_mlp_dict = build_embedding_layers(dnn_features_columns, sparse_input_dict, prefix=\"MLP\", is_linear=False)\n",
    "    \n",
    "    # 4. 将Embedding后的特征进行展开，因为Embedding后为(?,1,8)\n",
    "    gml_user_embedding = Flatten()(embedding_gml_dict['user_id'](sparse_input_dict['user_id']))\n",
    "    gml_movie_embedding = Flatten()(embedding_gml_dict['movie_id'](sparse_input_dict['movie_id']))\n",
    "    \n",
    "    mlp_user_embedding = Flatten()(embedding_mlp_dict['user_id'](sparse_input_dict['user_id']))\n",
    "    mlp_movie_embedding = Flatten()(embedding_mlp_dict['movie_id'](sparse_input_dict['movie_id']))\n",
    "    \n",
    "    # 5. 进行GML，就是展开后的特征进行内积\n",
    "    gml_output = build_gml_layers(gml_user_embedding, gml_movie_embedding)\n",
    "#     gml_output = tf.multiply(gml_movie_embedding, gml_user_embedding)\n",
    "#     gml_output = Multiply()([gml_user_embedding, gml_movie_embedding])\n",
    "    \n",
    "    # 6. 进行MLP，将特征进行连接，传入MLP层\n",
    "    mlp_input = Concatenate(axis=1)([mlp_user_embedding, mlp_movie_embedding])\n",
    "    mlp_output = build_mlp_layers(mlp_input, (32, 16))\n",
    "    \n",
    "    # 7. 将GML和MLP层的输出进行连接\n",
    "    concat_output = Concatenate(axis=1)([gml_output, mlp_output])\n",
    "    \n",
    "    # 8.传入到输出层中，获取评分\n",
    "    output_layers = bulid_output_layers(concat_output)\n",
    "    \n",
    "    # 构建模型\n",
    "    model = Model(input_layers, output_layers)\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31ed9ae",
   "metadata": {},
   "source": [
    "### 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "34f432a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.9432 - mae: 0.7547 - val_loss: 0.9894 - val_mae: 0.8008\n",
      "Epoch 2/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.7478 - mae: 0.6812 - val_loss: 0.9943 - val_mae: 0.8067\n",
      "Epoch 3/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.6981 - mae: 0.6561 - val_loss: 1.0470 - val_mae: 0.8421\n",
      "Epoch 4/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.6755 - mae: 0.6445 - val_loss: 1.0259 - val_mae: 0.8291\n",
      "Epoch 5/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.6621 - mae: 0.6377 - val_loss: 1.0353 - val_mae: 0.8364\n",
      "Epoch 6/10\n",
      "6252/6252 [==============================] - 15s 2ms/step - loss: 0.6536 - mae: 0.6331 - val_loss: 1.0343 - val_mae: 0.8335\n",
      "Epoch 7/10\n",
      "6252/6252 [==============================] - 16s 3ms/step - loss: 0.6469 - mae: 0.6296 - val_loss: 1.0350 - val_mae: 0.8369\n",
      "Epoch 8/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.6421 - mae: 0.6271 - val_loss: 1.0521 - val_mae: 0.8483\n",
      "Epoch 9/10\n",
      "6252/6252 [==============================] - 15s 2ms/step - loss: 0.6382 - mae: 0.6252 - val_loss: 1.0593 - val_mae: 0.8498\n",
      "Epoch 10/10\n",
      "6252/6252 [==============================] - 14s 2ms/step - loss: 0.6347 - mae: 0.6232 - val_loss: 1.0730 - val_mae: 0.8582\n"
     ]
    }
   ],
   "source": [
    "ncf = NCF(dnn_features_columns)\n",
    "\n",
    "# 编译模型\n",
    "ncf.compile(optimizer=\"adam\", \n",
    "                loss=\"mse\", \n",
    "                metrics=['mae'])\n",
    "\n",
    "# 训练数据做成字典，与输入层做对应\n",
    "train_model_input = {name: train_data[name] for name in ['user_id', 'movie_id']}\n",
    "\n",
    "history = ncf.fit(train_model_input, \n",
    "            train_data['label'].values,\n",
    "            batch_size=128, \n",
    "            epochs=10, \n",
    "            validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dc82db71",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31257/31257 [==============================] - 38s 1ms/step - loss: 0.6955 - mae: 0.6574\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.695504367351532, 0.6573657393455505]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_model_input = {name: train_data[name] for name in ['user_id', 'movie_id']}\n",
    "ncf.evaluate(test_model_input,train_data['label'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93821ebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a5863af3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制网络结构图\n",
    "plot_model(ncf,show_shapes=True,dpi=280)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49538467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制网络结构图\n",
    "plot_model(ncf,show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af7106e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff373b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18a0376b208>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mae = history.history['mae']\n",
    "loss = history.history['loss']\n",
    "val_mae = history.history['val_mae']\n",
    "val_loss = history.history['val_loss']\n",
    "# ROC = history.history['auc_6']\n",
    "# PR = history.history['auc_7']\n",
    "epochs = range(1, len(mae) + 1)\n",
    "# plt.subplot(1,3,1)\n",
    "plt.title('mae and Loss')\n",
    "plt.plot(epochs, mae, 'ro:', label='Training mae')\n",
    "plt.plot(epochs, loss, 'r.-', label='Training loss')\n",
    "plt.plot(epochs, val_mae, 'go:', label='Validation mae')\n",
    "plt.plot(epochs, val_loss, 'g.-', label='Validation loss')\n",
    "plt.xticks(ticks=epochs)\n",
    "plt.yticks(ticks=np.arange(0.1,2,0.1))\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf2",
   "language": "python",
   "name": "tf2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
